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Introduction

MindBridge's ability to integrate with Databricks enables seamless automation of financial data ingestion, transformation, and analysis, enhancing financial risk detection capabilities.

This document provides an overview of how your team can integrate MindBridge with Databricks, and offers additional context and guidance for implementation and developer teams within MindBridge customer organizations. This guide keeps the process straightforward and provides links to downloadable Python notebook templates (available on GitHub) for detailed implementation.

How MindBridge’s Databricks integration works

MindBridge has developed a set of Databricks notebook templates to support common use cases, ensuring a streamlined integration between Databricks and MindBridge.

Key aspects of the integration

• Pre-configured Python notebooks: Access reusable Python notebooks designed to run within Databricks for tasks such as sending data to MindBridge, triggering analyses, and querying results.

• Customization: Easily customize these notebooks to meet specific needs, such as adjusting data frequency, integrating custom data sources, or modifying analysis parameters.

• Resources and tutorials: Leverage supporting documentation to help your team quickly set up and customize the notebooks for your environment.

Getting started

Downloadable Python notebooks for Databricks

MindBridge has created a series of Python-based notebooks that you can run in your Databricks environment. These notebooks allow you to orchestrate MindBridge workflows directly from Databricks. Documentation within the notebooks provides guidance for user-friendly setup and customization.

1. MindBridge_E2E_Integration_Tutorial - Provides a how-to guide for setting up a MindBridge integration with Databricks, covering steps like setting up an Organization and Engagement, performing a General Ledger analysis, and extracting key information from analysis results.
2. MindBridge_Explore_GL_Table - Provides an example of querying from a Databricks database of General Ledger data to create a file that can be leveraged as input into any MindBridge analysis.
3. MindBridge_E2E_Analysis_Data_Flow - Demonstrates a complete initial configuration of a MindBridge organization, engagement, and analysis. It includes uploading data from Databricks to MindBridge, executing an analysis, and generating a link to view results in MindBridge.
4. MindBridge_Update_Data_Run_Existing_Analysis - Illustrates the process of updating data (e.g., adding new transactional data) from Databricks and running an existing analysis in MindBridge without modifying the original configuration. This use case supports periodic analysis needs, allowing for refreshed data insights based on the latest data updates.
5. MindBridge_Results_Integration_Example - Showcases how to pull MindBridge results back into Databricks, enabling further analysis using downstream tools. This allows users to integrate MindBridge results into dashboards or reports for deeper insight and communication.

Access the notebook templates

Download the preconfigured Python notebooks for Databricks from GitHub

Example use case

Scenario

A global technology company uses Databricks as its centralized data platform, updating it nightly with ERP and operational data. To streamline workflows, they prefer MindBridge to source data directly from Databricks, reducing the need for multiple systems to access operational environments.

Example Workflow

Using the MindBridge integration notebooks with Databricks, the company enables a fully automated, end-to-end financial analytics workflow:

• Data preparation and transformation

A scheduled Databricks job prepares ERP and operational data by applying reusable, predefined transformation logic. Cleaned and structured datasets are formatted to match MindBridge requirements and pushed directly into MindBridge via API.

• Automated analysis orchestration

The same Databricks Notebook automates MindBridge API calls to trigger new analyses as soon as data is ready. This orchestration ensures that every data refresh (e.g., nightly or weekly) results in timely, consistent financial analysis—without manual intervention.

• Frictionless insights delivery

With automated data delivery and analysis execution, business users can access the latest results in MindBridge as part of their daily workflows. No separate data pulls or manual uploads are required.

• Integrated risk intelligence

Risk scores and analysis results are programmatically retrieved via API back into Databricks. The company leverages MindBridge outputs to conduct deeper investigations and enrich internal dashboards—enabling continuous risk monitoring and integrated reporting alongside other business KPIs.

Benefits

• Operational efficiency: Streamlines operations and eliminates the need for custom scripts.
• Focused risk mitigation: Enables finance teams to concentrate on reviewing and mitigating risks more effectively.
• Seamless workflow: Maintains consistent workflows within the Databricks environment.